摘要： 体素内不相干运动（Intravoxel Incoherent Motion，IVIM）是一种无需造影剂即可无创评估组织水分子扩散系数（D）与血液灌注信息（F，D*）等 IVIM 参数的扩散加权磁共振成像（Diffusion-Weighted Magnetic Resonance Imaging，DWI）分析技术，该扩散和灌注信息对肝癌、脑肿瘤等疾病的诊断和治疗具有重要意义。现有研究多致力于从 DWI 信号中捕获组织空间相似特征辅助IVIM参数估计，从而提高对噪声的鲁棒性，但往往忽略了 DWI 信号本身存在的局限性，当信号受噪声污染严重时，信号中蕴含的生理特征被破坏，特征之间的相似性关联难以精准捕获。本文受深度图像先验（Deep Image Prior，DIP）原理启发，发现训练中的 DWI 信号存在信号质量差异现象（Differences in Signal Quality，DSQ），并且基于该现象提出了一种在训练过程中提高训练集质量的策略。具体而言， DSQ 现象表明在模型深入训练后，部分拟合信号的整体质量逐渐优于原始训练信号，因此本文利用拟合信号作指引对原始训练信号进行优化，同时为了增强数据优化的效果，通过迭代策略调控数据优化过程，逐步提升原始训练信号的质量，降低噪声对拟合过程的干扰。此外，特征间的相似性在局部邻域与全局空间中同样广泛存在，而现有基于卷积的方法受感受野大小限制难以有效捕获全局信息；基于自注意力的方法虽能感知全局体素之间的相关性，但往往会牺牲局部高频细节。考虑到属于相同组织的特征蕴含的生理信息接近，通常表现出更高的相似性，本文提出了一种基于组织先验引导的相似特征提取方式，该特征提取方式能够提升拟合信号质量从而提升迭代优化策略的效果，进一步提升 IVIM 参数估计的准确性。基于上述改进，提出了一种数据迭代神经网络（Data Iterative Neural Network，DI-NN）。为了验证方法的性能，将本文方法与传统方法，基于体素的深度学习方法以及基于空间相关性的深度学习方法对比，采用均方根误差（RMSE）、结构相似性指数（SSIM）、Mann-Whitney U检验等指标。 RMSE 结果显示，在信噪比（SNR）8~50的实验中，本文方法参数 RMSE 更低，参数预测更准确。例如在 SNR15 的实验中， IVIM 参数F、D和D*的 RMSE 分别比次优方法降低了37.51%、18.36%和30.92%。 SSIM 结果表明，所提方法保留结构特征的能力更强。 Mann-Whitney U 检验结果进一步说明所提方法拥有更强的区分病灶的能力。

Abstract: Intravoxel Incoherent Motion (IVIM) is a diffusion-weighted magnetic resonance imaging (DWI) analysis technique that enables noninvasive assessment of the tissue diffusion coefficient (D) and perfusion-related parameters (F, D*) without contrast agents, which are clinically important for the diagnosis and treatment of diseases such as liver cancer and brain tumors. Existing studies mainly focus on capturing spatially similar tissue features from DWI signals to assist IVIM parameter estimation and thus improve robustness to noise. However, they often ignore the inherent limitations of the DWI signals themselves: when the signals are severely contaminated by noise, the physiological characteristics contained in the signals are damaged, making it difficult to accurately capture the similarity relationships among features. Inspired by the principle of Deep Image Prior (DIP), this study identifies a phenomenon of Differences in Signal Quality (DSQ) in DWI signals during training, and further proposes a strategy for improving the quality of the training set during the training process based on this phenomenon. Specifically, the DSQ phenomenon indicates that, as training progresses, the overall quality of some fitted signals gradually becomes better than that of the original training signals. Therefore, the fitted signals are used as guidance to optimize the original training signals. Meanwhile, to further enhance the effect of data optimization, an iterative strategy is introduced to regulate the data optimization process, progressively improving the quality of the training signals and reducing the influence of noise on the fitting process. In addition, similarity among features is also widely present in both local neighborhoods and global space. Existing convolution-based methods are limited by the size of the receptive field and thus have difficulty in effectively capturing global information, whereas self-attention-based methods can perceive global correlations among voxels but often sacrifice local high-frequency details. Considering that features belonging to the same tissue usually contain similar physiological information and exhibit higher similarity, a tissue-prior-guided similar feature extraction scheme is proposed in this study. This scheme improves the quality of the fitted signals, thereby enhancing the effectiveness of the iterative optimization strategy and further improving the accuracy of IVIM parameter estimation. Based on the above improvements, a Data Iterative Neural Network (DI-NN) is proposed. To evaluate the performance of the model, the proposed DI-NN is compared with the traditional, voxel-based deep learning, and spatial-correlation-based deep learning methods. Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), and the Mann-Whitney U test are used as evaluation metrics. RMSE results show that across experiments with SNRs ranging from 8 to 50, DI-NN consistently achieves lower RMSE values and more accurate parameter estimation. For example, at SNR 15, the RMSEs of IVIM parameters F, D, and D* are reduced by 37.51%, 18.36%, and 30.92%, respectively, compared with the suboptimal method. SSIM results indicate that DI-NN preserves structural features better than other methods. The Mann-Whitney U test further demonstrates that DI-NN has a stronger capability to distinguish lesions.